Abstract
Conversational semantic role labeling (CSRL) is believed to be a crucial step towards dialogue understanding. However, it remains a major challenge for existing CSRL parser to handle conversational structural information. In this paper, we present a simple and effective architecture for CSRL which aims to address this problem. Our model is based on a conversational structure aware graph network which explicitly encodes the speaker dependent information. We also propose a multi-task learning method to further improve the model. Experimental results on benchmark datasets show that our model with our proposed training objectives significantly outperforms previous baselines.- Anthology ID:
- 2021.emnlp-main.177
- Volume:
- Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
- Month:
- November
- Year:
- 2021
- Address:
- Online and Punta Cana, Dominican Republic
- Editors:
- Marie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-tau Yih
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2312–2317
- Language:
- URL:
- https://aclanthology.org/2021.emnlp-main.177
- DOI:
- 10.18653/v1/2021.emnlp-main.177
- Cite (ACL):
- Han Wu, Kun Xu, and Linqi Song. 2021. CSAGN: Conversational Structure Aware Graph Network for Conversational Semantic Role Labeling. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 2312–2317, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
- Cite (Informal):
- CSAGN: Conversational Structure Aware Graph Network for Conversational Semantic Role Labeling (Wu et al., EMNLP 2021)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2021.emnlp-main.177.pdf
- Code
- hahahawu/CSAGN